arXiv:2607.05104v1 Announce Type: cross Abstract: Grokking -- the delayed onset of generalization long after a network has fit its training set - -is usually studied in models too large to read completely and reported from single training runs. We instead study a publicly released ~11,856-parameter Llama-style transformer (Glimmer-1-Base) on modular arithmetic, small enough to enumerate its weights, attention, and full input-output map, and we measure grokking as a multi-seed rate rather than a single outcome. In this fully-tractable regime grokking is a conditional, fragile phase transition.
This research provides a deeper, more tractable understanding of 'grokking' in AI models, a phenomenon that has previously been challenging to analyze due to model size and single-run reporting.
A more thorough understanding of AI model generalization (grokking), especially its conditional and fragile nature, is crucial for developing robust and reliable AI systems.
The research shifts the understanding of grokking from a singular outcome to a multi-seed rate, highlighting its inherent fragility and conditionality in smaller, tractable models.
- · AI researchers
- · AI safety groups
- · Developers of foundational models
- · Developers relying on 'black box' grokking
- · AI systems vulnerable to fragility
This research will inform better design principles for AI models to ensure more reliable generalization.
Improved understanding of grokking could accelerate the development of more stable and predictable AI agents.
More robust AI systems might mitigate some of the risks associated with AI deployment, potentially influencing regulatory approaches.
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Read at arXiv cs.AI